Systematic physics constrained parameter estimation of stochastic differential equations

@article{Peavoy2013SystematicPC,
  title={Systematic physics constrained parameter estimation of stochastic differential equations},
  author={Daniel Peavoy and Christian L. E. Franzke and Gareth O. Roberts},
  journal={Comput. Stat. Data Anal.},
  year={2013},
  volume={83},
  pages={182-199}
}
View PDF on arXiv
17 Citations
Highly Influential Citations
1
Background Citations
9
Methods Citations
7

17 Citations

New Trends in Ensemble Forecast Strategy: Uncertainty Quantification for Coarse-Grid Computational Fluid Dynamics

A new energy-budget-based stochastic subgrid scheme and a new way of parameterizing models under location uncertainty, both of which are based on randomized-initial-condition methods, are introduced.

New Trends in Ensemble Forecast Strategy: Uncertainty Quantification for Coarse-Grid Computational Fluid Dynamics

Numerical simulations of industrial and geophysical fluid flows cannot usually solve the exact Navier–Stokes equations. Accordingly, they encompass strong local errors. For some applications—like…

Stochastic Climate Theory

This chapter provides a conceptual framework for stochastic modelling of deterministic dynamical systems based on the Mori-Zwanzig formalism and expresses standard model reduction methods such as averaging and homogenization which eliminate the memory term.

Reduced-space Gaussian Process Regression for data-driven probabilistic forecast of chaotic dynamical systems

Mixing and fluid dynamics under location uncertainty

This thesis develops, analyzes and demonstrates several valuable applications of randomized fluid dynamics models referred to as under location uncertainty. The velocity is decomposed between…

Comparison of stochastic parameterizations in the framework of a coupled ocean–atmosphere model

Abstract. A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the context of the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM), a coupled…

Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models and an overview of stochastic climate theory from an applied mathematics perspective is provided.

UvA-DARE (Digital Academic Repository) Stochastic climate theory

This chapter provides a conceptual framework for stochastic modelling of deterministic dynamical systems based on the Mori-Zwanzig formalism and expresses standard model reduction methods such as averaging and homogenization which eliminate the memory term.

Calculating State-Dependent Noise in a Linear Inverse Model Framework

AbstractThe most commonly used version of a linear inverse model (LIM) is forced by state-independent noise. Although having several desirable qualities, this formulation can only generate long-term…

Inverse stochastic–dynamic models for high-resolution Greenland ice core records

Abstract. Proxy records from Greenland ice cores have been studied for several decades, yet many open questions remain regarding the climate variability encoded therein. Here, we use a Bayesian…

References

SHOWING 1-10 OF 41 REFERENCES

A mathematical framework for stochastic climate models

There has been a recent burst of activity in the atmosphere‐ocean sciences community in utilizing stable linear Langevin stochastic models for the unresolved degrees of freedom in stochastic climate…

Methods of likelihood based inference for constructing stochastic climate models

This thesis is about the construction of low dimensional diffusion models of climate variables. It assesses the predictive skill of models derived from a principled averaging procedure and a purely…

An applied mathematics perspective on stochastic modelling for climate

  • A. MajdaC. FranzkeB. Khouider
  • Environmental Science
    Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
  • 2008
A new low-dimensional stochastic model is developed here, which mimics key features of atmospheric general circulation models, to test the fidelity of Stochastic mode reduction procedures.

Models for stochastic climate prediction.

The strategy for stochastic climate modeling that emerges from this analysis is illustrated on an idealized example involving truncated barotropic flow on a beta-plane with topography and a mean flow.

Normal forms for reduced stochastic climate models

The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high-dimensional climate models is an important topic for atmospheric low-frequency…

Estimation for nonlinear stochastic differential equations by a local linearization method 1

This paper proposes a new local linearization method which approximates a nonlinear stochastic differential equation by a linear stochastic differential equation. Using this method, we can estimate…

Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time Diffusion Processes

Stochastic differential equations often provide a convenient way to describe the dynamics of economic and financial data, and a great deal of effort has been expended searching for efficient ways to…

Data Assimilation in Slow–Fast Systems Using Homogenized Climate Models

The conclusion is that stochastic parameterizations are attractive for data assimilation despite their sensitivity to uncertainties in the parameters, which is a natural way to provide sufficient ensemble spread to detect transitions between regimes.

Bayesian inference for nonlinear multivariate diffusion models observed with error

Low-Order Stochastic Mode Reduction for a Realistic Barotropic Model Climate

Abstract This study applies a systematic strategy for stochastic modeling of atmospheric low-frequency variability to a realistic barotropic model climate. This barotropic model climate has…